/*
 *    MajorityClass.java
 *    Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
 *    @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 *
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */
package moa.classifiers;

import moa.core.DoubleVector;
import moa.core.Measurement;
import moa.core.StringUtils;
import weka.core.Instance;

public class MajorityClass extends AbstractClassifier {

	private static final long serialVersionUID = 1L;

	@SuppressWarnings("hiding")
	public static final String classifierPurposeString = "Majority class classifier: always predicts the class that has been observed most frequently the in the training data.";

	protected DoubleVector observedClassDistribution;

	@Override
	public void resetLearningImpl() {
		this.observedClassDistribution = new DoubleVector();
	}

	@Override
	public void trainOnInstanceImpl(Instance inst) {
		this.observedClassDistribution.addToValue((int) inst.classValue(), inst
				.weight());
	}

	public double[] getVotesForInstance(Instance i) {
		return this.observedClassDistribution.getArrayCopy();
	}

	@Override
	protected Measurement[] getModelMeasurementsImpl() {
		return null;
	}

	@Override
	public void getModelDescription(StringBuilder out, int indent) {
		StringUtils.appendIndented(out, indent, "Predicted majority ");
		out.append(getClassNameString());
		out.append(" = ");
		out.append(getClassLabelString(this.observedClassDistribution
				.maxIndex()));
		StringUtils.appendNewline(out);
		for (int i = 0; i < this.observedClassDistribution.numValues(); i++) {
			StringUtils.appendIndented(out, indent, "Observed weight of ");
			out.append(getClassLabelString(i));
			out.append(": ");
			out.append(this.observedClassDistribution.getValue(i));
			StringUtils.appendNewline(out);
		}
	}

	public boolean isRandomizable() {
		return false;
	}

}
